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Monday, April 20, 2020 | History

1 edition of Querying Moving Objects Detected by Sensor Networks found in the catalog.

Querying Moving Objects Detected by Sensor Networks

  • 287 Want to read
  • 35 Currently reading

Published by Springer New York, Imprint: Springer in New York, NY .
Written in English

    Subjects:
  • Computer Imaging, Vision, Pattern Recognition and Graphics,
  • Networks Communications Engineering,
  • Telecommunication,
  • Computer science,
  • Computer vision,
  • Artificial intelligence,
  • Artificial Intelligence (incl. Robotics)

  • About the Edition

    Declarative query interfaces to Sensor Networks (SN) have become a commodity. These interfaces allow access to SN deployed for collecting data using relational queries. However, SN are not confined to data collection, but may track object movement, e.g., wildlife observation or traffic monitoring. While rational approaches are well suited for data collection, research on Moving Object Databases (MOD) has shown that relational operators are unsuitable to express information needs on object movement, i.e., spatio-temporal queries. Querying Moving Objects Detected by Sensor Networks studies declarative access to SN that track moving objects. The properties of SN present a straightforward application of MOD, e.g., node failures, limited detection ranges and accuracy which vary over time etc. Furthermore, point sets used to model MOD-entities like regions assume the availability of very accurate knowledge regarding the spatial extend of these entities, assuming such knowledge is unrealistic for most SN. This book is the first that defines a complete set of spatio-temporal operators for SN while taking into account their properties. Based on these operators, we systematically investigate how to derive query results from object detections by SN. Finally, process spatio-temporal queries are shown in SN efficiently, i.e., reducing the communication between nodes. The evaluation shows that the measures reduce communication by 45%-89%.

    Edition Notes

    Statementby Markus Bestehorn
    SeriesSpringerBriefs in Computer Science
    ContributionsSpringerLink (Online service)
    Classifications
    LC ClassificationsT385, TA1637-1638, TK7882.P3
    The Physical Object
    Format[electronic resource] /
    PaginationXII, 71 p. 29 illus.
    Number of Pages71
    ID Numbers
    Open LibraryOL27084216M
    ISBN 109781461449270


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Querying Moving Objects Detected by Sensor Networks by Markus Bestehorn Download PDF EPUB FB2

Querying Moving Objects Detected by Sensor Networks studies declarative access to SN that track moving objects. The properties of SN present a straightforward application of MOD, e.g., node failures, limited detection ranges and accuracy which vary over time etc.

Querying Moving Objects Detected by Sensor Networks. R.H., et al.: Modeling and querying moving objects in networks. VLDB J. () Google Scholar. Bestehorn M. () Querying Moving Objects Detected by Sensor Networks. In: Querying Moving Objects Detected by Sensor Networks.

SpringerBriefs in Computer Science. Springer, New York Author: Markus Bestehorn. Get this from a library. Querying moving objects detected by sensor networks. [Markus Bestehorn] -- Declarative query interfaces to Sensor Networks (SN) have become a commodity.

These interfaces allow access to SN deployed for collecting data using relational queries. However, SN are not confined. Querying Moving Objects Detected by Sensor Networks Extended Version Markus Bestehorn Version of June 5, Abstract Declarative query interfaces to Sensor Net-works (SN) have become a commodity.

These interfaces allow access to SN deployed for collecting data using re-lational queries. However, SN are not confined to data. Querying Moving Objects Detected by Sensor Networks studies declarative access to SN that track moving objects.

The properties of SN present a straightforward application of MOD, e.g., node failures, limited detection ranges and accuracy which vary over time etc. Furthermore, point sets used to model MOD-entities like regions assume the Author: Markus Bestehorn. Kup książkę Querying Moving Objects Detected by Sensor Networks (Markus Bestehorn) Querying Moving Objects Detected by Sensor Networks book jedyne zł u Querying Moving Objects Detected by Sensor Networks book godnego zaufania.

Zajrzyj do środka, czytaj Querying Moving Objects Detected by Sensor Networks book innych czytelników, pozwól nam polecić Ci podobne tytuły z naszej ponad milionowej kolekcji. Querying Moving Objects Detected by Sensor Networks studies declarative access to SN that track moving objects.

The properties of SN present a straightforward application Querying Moving Objects Detected by Sensor Networks book MOD, e.g., node failures, limited detection ranges and accuracy which vary over time : Springer New York.

Buy (ebook) Querying Moving Objects Detected by Sensor Networks by Markus Bestehorn, eBook format, from the Dymocks online bookstore. Querying and Tasking in Sensor Networks. and identifying detected objects and are pre-loaded with the features of the tracked objects before they are deployed.

protocols for wireless. Querying Moving Objects in SECONDO (geo-sensor networks, moving objects tracking, real-time traffic Querying Moving Objects Detected by Sensor Networks book, etc.) process huge volumes of continuous data streams, i.e.

data sets that are. While rational approaches are well suited for data collection, research on Moving Object Databases (MOD) has shown that relational operators are unsuitable to express information needs on object movement, i.e., spatio-temporal ng Moving Objects Detected by Sensor Networks studies declarative access to SN that track moving objects.

Querying Moving Objects Detected by Sensor Networks Declarative query interfaces to Sensor Networks (SN) have become a commodity. These interfaces allow access to SN deployed for collecting data using relational queries. However, SN are not confined to data collection, but may track object movement, e.g., wildlife observation or traffic monitoring.

The rst work to combine models, declarative SQL-like queries and live data acquisition in sensor networks was the BBQ System [7, 6]. The authors proposed a general architecture for model-based querying, and posed the optimization problem of selecting the best sensor readings to acquire to satisfy a user query (which can be seen.

The ultrasonic signal received during a measurement period τ is sampled n times and represented by s(k), a binary data sequence which represents the existence of an object by 1 and the absence bythe distance d(k) to the object represented by s(k) can be calculated by Eq.(1), where c is the speed of the ultrasonic signal in air.

(1) d(k)= c 2 k n τs(k) (k=1,2,3,n).Cited by: 7. An extended license is needed in order to use ADAS sensors. However, moving objects are available with any license. Each sensor has a location fixed in some part of the simulated vehicle, with a designated aiming direction and sensitivity to radiation pattern and range.

The sprung mass is the default location in all VehicleSim products. As sensor networks become more and more ubiquitous, engineers face the challenge of making the sensor data accessible to the common person. For example, a building manager might want to be alerted to excess building activity over the weekends, or a safety engineer might want a histogram of vehicle speeds in the parking garage.

However, [ ]. A data querying scheme is introduced for sensor networks where queries formed for each sensing task are sent to task sets. The sensor field is partitioned into subregions by using quadtree based addressing, and then a given number of sensors from each subregion are assigned to each task set by using a distributed by: 2.

Model-based Approximate Querying in Sensor Networks 3 sensors that are providing faulty data, and can extrapolate the values of missing sensors or sensor readings at geographic locations where sensors are no longer op-erational.

Furthermore, models provide a framework for optimizing the acquisition. Abstract: The accurate detection and classification of moving objects is a critical aspect of advanced driver assistance systems. We believe that by including the object classification from multiple sensor detections as a key component of the object's representation and the perception process, we can improve the perceived model of the by: TRACKING OF MOVING OBJECT IN WIRELESS SENSOR NETWORK YA & Department of Information Science and Technology, Anna University, Chennai, India E-mail: @ [email protected] Abstract - A Wireless Sensor Network is a collection of sensor nodes distributed into a network to monitor theFile Size: KB.

disseminated into the sensor network through other mobile objects. The results pulled out from the sensor network are then routed back to the querying objects via the mobile network layer. Moreover, previous works assume that users and mobile base stations play two distinct roles in sensor networks, with base.

10 PCS New LJ12AZ/BY Inductive Proximity Sensor Switch PNP DCV US 10 PCS New. PCS LJ12AZ/BY New 10 US Inductive DCV Switch Sensor Proximity PNP PNP Proximity Sensor PCS Inductive DCV LJ12AZ/BY Switch 10 New US. $ tracking of the moving objects, and on the relationship of their trajectories to the scene.

In this paper, we address the problem of detecting and tracking moving objects in the context of video surveillance.

Most of the techniques used for this problem deal with a sta-tionary File Size: KB. networks by taking advantage of the more powerful mobile devices.

We present a systematic framework for end-to-end query processing, using a two-layer architecture that consists of mobile devices at the upper layer and static sensor nodes at the bottom layer. The Moving Object Detection (MOD) system can inform the driver of moving objects when driving out of garages, maneuvering into parking lots and in other such instances.

The MOD system detects moving objects by using image processing technology on the image shown in the display. and classification algorithm for moving objects. Compared to outdoor detection, indoor detection and tracking is simplified because in general the ground is flat, objects are mostly vertical, the environment is limited by walls, and moving cars and vegetation are basically nonex-isting.

On the other hand, one can encounter large groups of Cited by: Update-e–cient Indexing of Moving Objects in Road Networks Jidong Chen Xiaofeng Meng Yanyan Guo Zhen Xiao School of Information, Renmin University of China fchenjd, xfmeng, guoyy, [email protected] Abstract Recent advances in wireless sensor networks and positioning technologies have boosted new applications that manage moving objects.

Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking R. Omar Chavez-Garcia and Olivier Aycard Abstract—The accurate detection and classification of mov-ing objects is a critical aspect of Advanced Driver Assistance Systems (ADAS).

We believe that by including the objectsCited by: MOVING OBJECT DETECTION, TRACKING AND CLASSIFICATION FOR SMART VIDEO SURVEILLANCE Yi˘githan Dedeo˘glu M.S.

in Computer Engineering Supervisor: Assist. Prof. U˘gur Gud¨ ukba¨ y August, Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and.

The ACQUIRE mechanism for efficient querying in sensor networks Narayanan Sadagopan †, Bhaskar Krishnamachari §†, and Ahmed Helmy § †Department of Computer Science, University of Southern California, Los Angeles, CAUSA [email protected] §Department of Electrical Engineering - Systems, University of Southern California, Los Angeles, CAUSA.

Querying Moving Objects Detected by Sensor Networks. Querying Moving Objects Detected by Sensor Networks, A New Filtration Method and a Hybrid Strategy for Approximate String Matching.

Advances in Intelligent Systems and Applications - Volume 1, SIAM Journal on ComputingAbstract | PDF ( KB) Cited by: The emergence of video surveillance is the most promising solution for people living independently in their home.

Recently several contributions for video surveillance have been proposed. However, a robust video surveillance algorithm is still a challenging task because of illumination changes, rapid variations in target appearance, similar nontarget objects in background, and by: The ACQUIRE Mechanism for Efficient Querying in Sensor Networks Narayanan Sadagopan y, Bhaskar Krishnamachari xy, and Ahmed Helmy x yDepartment of Computer Science, University of Southern California, Los Angeles, CAUSA [email protected] xDepartment of Electrical Engineering - Systems, University of Southern California, Los Angeles, CAUSA.

The detection and tracking of moving objects (DATMO) in an outdoor environment from a mobile robot are difficult tasks because of the wide variety of dynamic objects. A reliable discrimination of mobile and static detections without any prior knowledge is often conditioned by a good position estimation obtained using Global Positionning System/Differential Global Positioning System Cited by: 7.

Type of Sensor – the presence of an object can be detected with proximity sensors, and there are several kinds of sensor technologies including here ultrasonic sensors, capacitive, photoelectric, inductive, or magnetic. Tracking objects can works using proximity sensors (ex.: ultrasonic sensors), or for advanced applications generally it is.

A motion sensor (or motion detector) is the linchpin of your security system, because it’s the main device that detects when someone is in your home when they shouldn’t be. A motion sensor uses one or multiple technologies to detect movement in an area.

If a sensor is tripped, a signal is sent to your security system’s control panel. Query Processing in Sensor Networks R ecent advances in computing tech-nology have led to the production of a new class of computing devices: the wireless, battery-powered, smart sensor.

Traditional sensors deployed throughout buildings, labs, and equipment are. the problem to track moving objects using a smart sensor network. Their work was mainly based on two assumptions.

One is that a sensor node is able to detect the existence of the moving object(s) when the objects falls in its sensing range. The other assumption is that the sensor has already learned the sensor reading to distance mapping. In [3.

To obtain data from a certain sensor, a user (Alice) forms a query and sends it to GW, which, in turn, communicates with 1Sample of the data complement is transmitted to the base station instead of the actual data. the relevant sensor(s). The queried sensor performs requested measurements and returns the results to Alice, again, via GW.

Monitoring Moving Objects using Low Frequency Snapshots in Sensor Networks Egemen Tanin NEC Labs, Cupertino, CA Univ. of Melbourne, Australia Songting Chen NEC Labs, Cupertino, CA Junichi Tatemura NEC Labs, Cupertino, CA Wang-Pin Hsiung NEC Labs, Cupertino, CA Abstract Monitoring moving objects is one of the key application domains for sensor.

With pdf expansion of smart agriculture, wireless sensor networks are being pdf applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates.

However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in Author: Jangsik Bae, Meonghun Lee, Changsun Shin.issues in object tracking are detecting the moving download pdf change in direction, varying speed of the object, object precision, prediction accuracy, fault tolerance and missing object recovery.

In all tracking process, more energy is consumed for messages or data transmission between the sensor nodes or between the sensor and sink [7].